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In this paper, we propose the scheme of multiple input representation-adaptive ensemble generation and aggregation(MIR-AEGA) for the classification of time series data. MIR-AEGA employs a set of heterogeneous classifiers, each of which takes a different representation of time series data as the input. MIR-AEGA adopts an "overfitting and selection" strategy. In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data ' globally in different degrees. The test data are then classified by each of the generated ensembles. The final decision is made by taking consideration into both the ability of each ensemble to fit the validation data locally and the possible overfitting effects. We claim that MIR-AEGA has two advantages (1) By using multiple representations, it exploits the temporal information of time series data as much as possible, thus could improve the overall performance (2) By tweaking the trade-off between the ability to fit the validation data and the overfitting effects, we expect the performance of this method is reliable in different situations. In this paper, the performance of MIR- AEGA is also assessed experimentally in comparison with other benchmark techniques. The experimental results demonstrate the good performance and the reliability of MIR-AEGA for the classification of time series data.
Date of Conference: 7-10 Oct. 2007